ONLINE ADVERTISEMENT FORECASTING USING TARGETED MESSAGES

• US 20160253709A1
• Filed: 03/09/2015
• Published: 09/01/2016
• Est. Priority Date: 02/27/2015
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: LinkedIn Corporation (Microsoft Corporation)
• Inventors: Deng, Zhifeng, Schellenberger, Jan, Chen, Jieying, Cui, Tingting, You, Siyu, Kumar, Deepak, Dong, Guangyu, Kshetramade, Sanjay
• Application Number:
  US14/642,586
• Publication Number:
  US 20160253709A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/0261 based on user location
• CPC Class Codes: G06Q 30/0264 based upon schedule
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

Techniques for forecasting for an advertisement campaign are described. A personalized communication system can receive a request for an advertisement campaign on a social network. The request can have a member attribute and a time frame. The personalized communication system can access member data and behavior data from the social network. Additionally, the personalized communication system can determine a target group based on the member data and the member attribute. Furthermore, the personalized communication system can calculate a number of unique visitors to the social network from the target group based on the member attribute, the time frame, and a frequency cap. Subsequently, the personalized communication system can forecast a number of messages for the first advertisement campaign based on the calculated number of unique visitors, the behavior data, and the time frame.

Claims

1. A method comprising:

receiving a request for a first advertisement campaign on a social network, the request having a member attribute and a time frame;

accessing member data from the social network;

determining a target group in the social network based on the member data and the member attribute;

accessing behavior data for the target group, the behavior data including a last logon date to the social network for a member;

calculating a number of unique visitors to the social network from the target group based on the member attribute, the time frame, and a frequency cap; and

forecasting, using a processor, a number of messages for the first advertisement campaign based on the calculated number of unique visitors, the behavior data, and the time frame.

2. The method of claim 1, wherein the time frame includes a start date and an end date, and wherein the forecasting includes:

calculating a discounting factor based on the start date, the end date, and the last logon date; and

calculating the number of messages based on the discounting factor and the number of unique visitors.

3. The method of claim 2, wherein the discounting factor is further based on the received member attribute.

4. The method of claim 1, wherein the frequency cap corresponds to a maximum number of messages that a member receives during a predetermined amount of time.

5. The method of claim 1, wherein the frequency cap is based on the member attribute.

6. The method of claim 1, wherein the request includes a geographic location, and wherein the target group is determined by:

determining a number of members living in the geographic location based on the assessed member data;

calculating a ratio of members in the social network having the member attribute based on the accessed member data; and

multiplying the number of members living in the geographic location by the ratio of members in the social network having the attribute.

7. The method of claim 6, wherein the calculated ratio is based on historical data of members living in the geographic location having the attribute.

8. The method of claim 1, wherein the time frame includes a first start date, and wherein calculating the number of unique visitors includes:

calculating the number of unique visitors to the social network from the target group based on the frequency cap;

accessing a similar member attribute from a second advertisement campaign, the second advertisement campaign having a second start date before the first start date of the first campaign;

identifying a degree of similarity between the similar member attribute and the received member attribute;

in response to the identification, reducing the number in the target group based on the first start date, the second start date; and

updating the calculated number of unique visitors based on the reduced number in the target group.

9. The method of claim 1, wherein the number of unique visitors is a number of daily unique visitors.

10. The method of claim 1, further comprising:

causing a presentation of the number of messages fur the first advertisement campaign on a display of a device.

11. The method of claim 1, wherein the member attribute is a job title.

12. The method of claim 1, wherein the member attribute is a job skill.

13. The method of claim 1, wherein the frequency cap is predetermined by the social network.

14. The method of claim 1, wherein the behavior data includes mobile device usage data of a member, and wherein the number of potential members corresponds to mobile device users.

15. The method of claim 1, wherein the behavior data includes desktop device usage data of a member, and wherein the number of potential members corresponds to desktop device users.

16. A social network system comprising:

a first database having profile data;

a second database having behavior data, the behavior data including a last logon date to a social network for a member;

one or more processors configured by a personalized communication module to;

receive a request for a first advertisement campaign on a social network, the request having a member attribute and a time frame;

access profile data from the first database;

determine a target group in the social network based on the profile data and the member attribute;

access the behavior data for the target group from the second database;

calculate a number of unique visitors to the social network from the target group based on the member attribute, the time frame, and a frequency cap; and

forecast a number of messages for the first advertisement campaign based on the calculated number of unique visitors, the behavior data, and the time frame.

17. The system of claim 16, wherein the personalized communication module is further configured to:

calculate a discounting factor based on the start date, the end date, and the last logon date; and

calculate the number of messages based on the discounting factor and the number of unique visitors.

18. The system of claim 16, wherein the personalized communication module is further configured to:

determine a number of members living in the geographic location based on the assessed member data;

calculate a ratio of members in the social network having the member attribute based on the accessed member data; and

multiply the number of members living the geographic location by the ratio of members in the social network having the attribute.

19. The system of claim 16, wherein the personalized communication module is further configured to:

calculate the number of unique visitors to the social network from the target group based on the frequency cap;

access a similar member attribute from a second advertisement campaign, the second advertisement campaign having a second start date before the first start date of the first campaign;

identify a degree of similarity between the similar member attribute and the received member attribute;

in response to the identification, reduce the number in the target group based on the first start date, the second start date; and

update the calculated number of unique visitors based on the reduced number in the target group.

20. A non-transitory machine-readable storage medium comprising instructions that, when executed by one or more processors of a machine, cause the machine to perform operations comprising:

receiving a request for a first advertisement campaign on a social network, the request having a member attribute and a time frame;

accessing member data from the social network;

determining a target group in the social network based on the member data and the member attribute;

accessing behavior data for the target group, the behavior data including a last logon date to the social network for a member;

calculating a number of unique visitors to the social network from the target group based on the member attribute, the time frame, and a frequency cap; and

forecasting, using a processor, a number of messages for the first advertisement campaign based on the calculated number of unique visitors, the behavior data, and the time frame.